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Measuring efficiency in retail planning

Marais, Kurt (2019-04)

Thesis (MCom)--Stellenbosch University, 2019.

Thesis

ENGLISH SUMMARY : Efficiency is the measure of how well a process performs, and businesses are constantly looking
for ways to improve their productivity. Traditional performance measures are commonly used and applied to data, but often do not consider the effect that multiple inputs and outputs have on the performance of a service unit. Thus, it is important to measure efficiency within the
current capabilities of service units. One way to measure the capabilities of efficiency is through benchmarking, which identifies best-practice service units and compares all service units to the best practices. The benchmarking tool used in this study that embodies this notion is known as data envelopment analysis. Data envelopment analysis (DEA) is a linear programming tool used to determine relative efficiency for a group of service units and provides a score on the level
of efficiency relative to other service units.
DEA is applied to the data of a prominent South African retailer, and multiple DEA models are applied to the data to provide insight into the efficiency of service units for the considered retailer. Numerous extensions and adaptations of DEA have been developed to provide deeper
insights into the efficiency of service units, depending on the available data. The CCR model and the BCC model are the main DEA models used in this thesis. Multiple regression analysis is also performed on the efficiency scores of DEA and the information that the models require.
Important components for DEA are the decision of inputs and outputs, as well as the number of service units considered at one time, all of which have an effect on the discriminatory power of the models. The data are grouped into categories and DEA is run on these groups to better
understand the results that DEA provides. The efficiency scores from the different models are determined for each of the considered service units order for the retailer to make decisions on minimising resources or maximising its outputs in future. DEA is not only a diagnostic tool for determining where inefficiencies exist, but how these inefficiencies should be approached, relative to best-practice units.
DEA results were applied to data of 1 207 stores over 26 weeks, and it was identified that new fashion products generally perform better than older products. Regression analysis used for
productivity measurement, while better for statistical analysis when compared to DEA, is limited in its ability to calculate efficiency for multiple inputs and multiple outputs at once. The results also provide confirmation on the discriminatory power of the choice of components used
in DEA, and that isolating one component as a measure of efficiency is not enough for service units, since performance is dependent on multiple factors. The overall result is that DEA be used in tandem with other performance measures to diagnose where inefficiencies occur, and use the information of DEA to move towards improved productivity.